You searched for subject:(supervised learning).
Showing records 1 – 30 of
652 total matches.
◁ [1] [2] [3] [4] [5] … [22] ▶
1.
Lease, Matthew A.
Beyond keywords: finding information more accurately and
easily using natural language.
Degree: PhD, Computer Science, 2009, Brown University
URL: https://repository.library.brown.edu/studio/item/bdr:206/ 
► Information retrieval (IR) has become a ubiquitous technology for quickly and easily finding information on a given topic amidst the wealth of digital content available…
(more)
▼ Information retrieval (IR) has become a ubiquitous
technology for quickly and easily finding information on a given
topic amidst the wealth of digital content available today. This
dissertation addresses search for written and spoken natural
language documents, including news articles, Web pages, and spoken
interviews. Effective model estimation is identified as a key
problem, and several novel estimation techniques are presented and
shown to significantly enhance search accuracy. While search is
typically performed via a few carefully chosen keywords,
formulating effective keyword queries is often unintuitive and
iterative, particularly when seeking complex information. As an
alternative to keyword search, this dissertation investigates
search using ``natural'' queries, such as questions or sentences a
person might naturally articulate in communicating their
information need to another person. By moving toward supporting
natural queries, the communication burden is shifted from user
query formulation to system interpretation of natural language. The
challenge in enacting such a shift is enabling automatic IR systems
to more effectively cope with natural language. To this end,
several new estimation techniques for modeling natural queries are
described. In comparison to a maximum likelihood baseline, 15-20%
relative improvement in mean average precision (MAP) is
demonstrated without use of query expansion. When an IR system
discovers or is provided one or more feedback documents
exemplifying a user's information need, there is further
opportunity to improve search accuracy by exploiting document
contents for query expansion. However, since documents typically
discuss multiple topics varying in importance and relevance to any
information need, the system must again be able to effectively
interpret verbose natural language. Consequently, an estimation
method for leveraging such documents is presented and shown to
yield state-of-the-art search accuracy. Depending on the base model
employed, 15-85% relative MAP improvement is achieved. When
modeling higher-order lexical features or searching smaller
document collections like cultural history archives, sparsity
become particularly problematic for estimation. To cope with such
sparsity, additional estimation methods are described which yield
5-20% relative improvement in MAP accuracy across varying
conditions of query verbosity.
Advisors/Committee Members: Charniak, Eugene (director), Johnson, Mark (reader), Allan, James (reader).
Subjects/Keywords: supervised learning
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Lease, M. A. (2009). Beyond keywords: finding information more accurately and
easily using natural language. (Doctoral Dissertation). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:206/
Chicago Manual of Style (16th Edition):
Lease, Matthew A. “Beyond keywords: finding information more accurately and
easily using natural language.” 2009. Doctoral Dissertation, Brown University. Accessed November 20, 2019.
https://repository.library.brown.edu/studio/item/bdr:206/.
MLA Handbook (7th Edition):
Lease, Matthew A. “Beyond keywords: finding information more accurately and
easily using natural language.” 2009. Web. 20 Nov 2019.
Vancouver:
Lease MA. Beyond keywords: finding information more accurately and
easily using natural language. [Internet] [Doctoral dissertation]. Brown University; 2009. [cited 2019 Nov 20].
Available from: https://repository.library.brown.edu/studio/item/bdr:206/.
Council of Science Editors:
Lease MA. Beyond keywords: finding information more accurately and
easily using natural language. [Doctoral Dissertation]. Brown University; 2009. Available from: https://repository.library.brown.edu/studio/item/bdr:206/
University of Illinois – Chicago
2.
Mohammadi, Neshat.
Supervised Tensor Learning with Applications.
Degree: 2017, University of Illinois – Chicago
URL: http://hdl.handle.net/10027/22099
► In this thesis, a new supervised tensor learning (STL) approach with application to neuroimages has been studied and implemented. We applied our proposed polynomial kernel-based…
(more)
▼ In this thesis, a new
supervised tensor
learning (STL) approach with application to neuroimages has been studied and implemented. We applied our proposed polynomial kernel-based approach in order to analyze HIV infections based on fMRI and DTI brain images. The goal of this project was to achieve a more accurate prediction for HIV diagnosis using fMRI and DTI images of the brain.
To achieve this goal we tried to improve the accuracy of the STL model by directly using tensor data as an input. Then, in order to solve STL problems, a structure-preserving feature mapping in addition to CP decomposed results has been defined to derive a Dual Structure-preserving Kernel (DuSK) in the tensor product feature space. Broadly, DuSK is a general framework to convert any vector-based kernel function to an equivalent tensorial representation.
Different from traditional STL frameworks, that usually intend to use linear models, our approach was based on a nonlinear kernel method and tensor factorization techniques that can preserve the multi- way structures of tensorial data. We investigated the performance of DuSK together with Support Vector Machine (SVM) for HIV infection classification on tensorial fMRI and matricized DTI data sets.
The experimental results are presented with details in evaluation chapter. According to our experiments, that DuSK with a nonlinear kernel can effectively boost classification performance in HIV data sets, and the choice of optimal kernel depends on the nature of the input data. Specifically, DuSK with an RBF kernel performs better on fMRI data, while DuSK with a polynomial kernel is better for DTI data.
Advisors/Committee Members: Yu, Philip S (advisor).
Subjects/Keywords: Supervised Tensor Learning
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Mohammadi, N. (2017). Supervised Tensor Learning with Applications. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/22099
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Mohammadi, Neshat. “Supervised Tensor Learning with Applications.” 2017. Thesis, University of Illinois – Chicago. Accessed November 20, 2019.
http://hdl.handle.net/10027/22099.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Mohammadi, Neshat. “Supervised Tensor Learning with Applications.” 2017. Web. 20 Nov 2019.
Vancouver:
Mohammadi N. Supervised Tensor Learning with Applications. [Internet] [Thesis]. University of Illinois – Chicago; 2017. [cited 2019 Nov 20].
Available from: http://hdl.handle.net/10027/22099.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Mohammadi N. Supervised Tensor Learning with Applications. [Thesis]. University of Illinois – Chicago; 2017. Available from: http://hdl.handle.net/10027/22099
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Oregon State University
3.
Hao, Guohua.
Revisiting output coding for sequential supervised learning.
Degree: MS, Computer Science, 2009, Oregon State University
URL: http://hdl.handle.net/1957/10897
► Markov models are commonly used for joint inference of label sequences. Unfortunately, inference scales quadratically in the number of labels, which is problematic for training…
(more)
▼ Markov models are commonly used for joint inference of label sequences. Unfortunately, inference scales quadratically in the number of labels, which is problematic for training methods where inference is repeatedly preformed and is the primary computational bottleneck for large label sets. Recent work has used output coding to address this issue by converting a problem with many labels to a set of problems with binary labels. Models were independently trained for each binary problem, at a much reduced computational cost, and then combined for joint inference over the original labels. Here we revisit this idea and show through experiments on synthetic and benchmark data sets that the approach can perform poorly when it is critical to explicitly capture the Markovian transition structure of the large-label problem. We then describe a simple cascade-training approach and show that it can improve performance on such problems with negligible computational overhead.
Advisors/Committee Members: Fern, Alan (advisor), Dietterich, Thomas (committee member).
Subjects/Keywords: ECOC; Supervised learning (Machine learning)
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Hao, G. (2009). Revisiting output coding for sequential supervised learning. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/10897
Chicago Manual of Style (16th Edition):
Hao, Guohua. “Revisiting output coding for sequential supervised learning.” 2009. Masters Thesis, Oregon State University. Accessed November 20, 2019.
http://hdl.handle.net/1957/10897.
MLA Handbook (7th Edition):
Hao, Guohua. “Revisiting output coding for sequential supervised learning.” 2009. Web. 20 Nov 2019.
Vancouver:
Hao G. Revisiting output coding for sequential supervised learning. [Internet] [Masters thesis]. Oregon State University; 2009. [cited 2019 Nov 20].
Available from: http://hdl.handle.net/1957/10897.
Council of Science Editors:
Hao G. Revisiting output coding for sequential supervised learning. [Masters Thesis]. Oregon State University; 2009. Available from: http://hdl.handle.net/1957/10897
Rutgers University
4.
Gazzola, Gianluca.
Supervised learning methods for variable importance and regression with uncertainty on dependent data.
Degree: PhD, Operations Research, 2019, Rutgers University
URL: https://rucore.libraries.rutgers.edu/rutgers-lib/60158/
► This dissertation covers a collection of supervised learning methods targeted to data with complex dependence patterns. Part of our work orbits around the concept of…
(more)
▼ This dissertation covers a collection of supervised learning methods targeted to data with complex dependence patterns. Part of our work orbits around the concept of variable importance, that is, the relative contribution an input variable to the prediction or the explanation of an output variable. Our interest in variable importance, and its estimation, is two-fold. On the one hand, as a tool for the characterization of data sets produced by multi-stage systems, where variables are related to each other via a network of correlations and causal dependencies. On the other hand, as a tool for the selection of minimal input-variable subsets with optimal predictive performance, in a more general framework involving data sets with an interesting structure of inter-variable dependence and redundancy. The rest of our work focuses on the problem of function approximation in the presence of uncertainty, and, specifically, on the calculation of optimal interpolating hyperplanes from data represented by convex polyhedra, rather than points. In this context, we propose algorithms to determine the spatial orientation of such polyhedra based on the multivariate relationships observed in the data, with particular focus on missing-value scenarios. For all of our methods, we present successful validation on an extensive and diverse array of real-world and simulated problems.
Advisors/Committee Members: Jeong, Myong K (chair), Boros, Endre (internal member), BEN-ISRAEL, ADI (internal member), Packard, Norman (outside member), CHAOVALITWONGSE, W. ART (outside member), Tortorella, Michael (outside member), School of Graduate Studies.
Subjects/Keywords: Supervised learning (Machine learning)
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Gazzola, G. (2019). Supervised learning methods for variable importance and regression with uncertainty on dependent data. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/60158/
Chicago Manual of Style (16th Edition):
Gazzola, Gianluca. “Supervised learning methods for variable importance and regression with uncertainty on dependent data.” 2019. Doctoral Dissertation, Rutgers University. Accessed November 20, 2019.
https://rucore.libraries.rutgers.edu/rutgers-lib/60158/.
MLA Handbook (7th Edition):
Gazzola, Gianluca. “Supervised learning methods for variable importance and regression with uncertainty on dependent data.” 2019. Web. 20 Nov 2019.
Vancouver:
Gazzola G. Supervised learning methods for variable importance and regression with uncertainty on dependent data. [Internet] [Doctoral dissertation]. Rutgers University; 2019. [cited 2019 Nov 20].
Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/60158/.
Council of Science Editors:
Gazzola G. Supervised learning methods for variable importance and regression with uncertainty on dependent data. [Doctoral Dissertation]. Rutgers University; 2019. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/60158/
Delft University of Technology
5.
Van Hecke, K.G.
Persistent self-supervised learning principle: Study and demonstration on flying robots:.
Degree: 2015, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:b722da02-089f-42a8-a3ea-fb3f5900bcdd
► We introduce, study and demonstrate Persistent Self-Supervised Learning (PSSL), a machine learning method for usage onboard robotic platforms. The PSSL model leverages a standard supervised…
(more)
▼ We introduce, study and demonstrate Persistent Self-
Supervised Learning (PSSL), a machine
learning method for usage onboard robotic platforms. The PSSL model leverages a standard
supervised learning method to simplify the
learning problem, but acquires training data in an unsupervised and autonomous manner. Using two platforms, a small multicopter on earth and the space based test bed SPHERES inside the International Space Station , we demonstrate the PSSL principle on a proof of concept problem:
learning monocular depth estimation using stereo vision. The robot operates first in a ground truth mode based on the distance perceived by the stereo system, while persistently
learning the environment using monocular cues. After the performance of the estimator transcends a ROC quality measure, the robot switches to operation based on the monocular depth estimates. Our results show the viability of the PSSL method, by being able to navigate a room on the basis of learned monocular vision, without collecting any training data beforehand. We identify a major challenge in PSSL caused by a training bias due to behavioral differences in the estimator and the ground truth based operation; however, this is a known problem also for related
learning methods such as reinforcement
learning. PSSL helps solve this problem by 1) clearly separating the
learning problem from the behavior and 2) the possibility to keep
learning during estimator behavior.
Advisors/Committee Members: De Croon, G.C.H.E., Van der Maaten, L.J.P., Izzo, D., Hennes, D..
Subjects/Keywords: persistent self-supervised learning; MAV
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Van Hecke, K. G. (2015). Persistent self-supervised learning principle: Study and demonstration on flying robots:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:b722da02-089f-42a8-a3ea-fb3f5900bcdd
Chicago Manual of Style (16th Edition):
Van Hecke, K G. “Persistent self-supervised learning principle: Study and demonstration on flying robots:.” 2015. Masters Thesis, Delft University of Technology. Accessed November 20, 2019.
http://resolver.tudelft.nl/uuid:b722da02-089f-42a8-a3ea-fb3f5900bcdd.
MLA Handbook (7th Edition):
Van Hecke, K G. “Persistent self-supervised learning principle: Study and demonstration on flying robots:.” 2015. Web. 20 Nov 2019.
Vancouver:
Van Hecke KG. Persistent self-supervised learning principle: Study and demonstration on flying robots:. [Internet] [Masters thesis]. Delft University of Technology; 2015. [cited 2019 Nov 20].
Available from: http://resolver.tudelft.nl/uuid:b722da02-089f-42a8-a3ea-fb3f5900bcdd.
Council of Science Editors:
Van Hecke KG. Persistent self-supervised learning principle: Study and demonstration on flying robots:. [Masters Thesis]. Delft University of Technology; 2015. Available from: http://resolver.tudelft.nl/uuid:b722da02-089f-42a8-a3ea-fb3f5900bcdd
Texas A&M University
6.
Perez, David Matthew.
Exploring Key Variables In Wind Turbine Power Curve Modeling.
Degree: MS, Industrial Engineering, 2018, Texas A&M University
URL: http://hdl.handle.net/1969.1/173925
► Though substantial evidence has shown the importance of wind speed and direction in modelling a wind turbine’s power curve, there remains uncertainty as to whether…
(more)
▼ Though substantial evidence has shown the importance of wind speed and direction in modelling a wind turbine’s power curve, there remains uncertainty as to whether other variables would improve modelling efforts and which specific variables those would be. This present work expands upon prior research using the additive multiplicative kernel (AMK) technique to explore the use of additional variables. The experimental methodology involves arriving at power estimates by treating a year’s amount of wind turbine data as a
learning problem. Different combinations of variables are investigated and compared in terms of error reduction on the testing set using root mean square error and mean average error. Discussion on the best sets of variables combinations are presented to gain insight as to why certain variables lead to greater error reduction and whether they are likely to be included in different sets of data.
Two categories of variables emerge from the research. The first includes variables that are typically recorded in field operations including time, turbulent intensity, and the standard deviation of wind direction. Time and turbulent intensity are shown to offer promising results. The next set includes variables that measure how much wind speed and direction vary as height varies. This is especially of interest as turbine size has increased substantially over recent years. In particular, the rotor equivalent wind speed neatly captures the variation of wind speed and direction across the length of a turbine’s rotor in a single value. Using this parameter with AMK leads to significant prediction error reduction, making a strong case to include it in modeling the power curve. As will be discussed, doing so proves to be a better alternative than current industry practice.
Advisors/Committee Members: Ding, Yu (advisor), Zeng, Li (committee member), Kezunovic, Mladen (committee member).
Subjects/Keywords: supervised learning; wind energy
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Perez, D. M. (2018). Exploring Key Variables In Wind Turbine Power Curve Modeling. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/173925
Chicago Manual of Style (16th Edition):
Perez, David Matthew. “Exploring Key Variables In Wind Turbine Power Curve Modeling.” 2018. Masters Thesis, Texas A&M University. Accessed November 20, 2019.
http://hdl.handle.net/1969.1/173925.
MLA Handbook (7th Edition):
Perez, David Matthew. “Exploring Key Variables In Wind Turbine Power Curve Modeling.” 2018. Web. 20 Nov 2019.
Vancouver:
Perez DM. Exploring Key Variables In Wind Turbine Power Curve Modeling. [Internet] [Masters thesis]. Texas A&M University; 2018. [cited 2019 Nov 20].
Available from: http://hdl.handle.net/1969.1/173925.
Council of Science Editors:
Perez DM. Exploring Key Variables In Wind Turbine Power Curve Modeling. [Masters Thesis]. Texas A&M University; 2018. Available from: http://hdl.handle.net/1969.1/173925
Colorado School of Mines
7.
Jackson, Ryan Blake.
Machine learning for encrypted Amazon Echo traffic classification.
Degree: MS(M.S.), Computer Science, 2018, Colorado School of Mines
URL: http://hdl.handle.net/11124/172223
► As smart speakers like the Amazon Echo become more popular, they have given rise to rampant concerns regarding user privacy. This work investigates machine learning…
(more)
▼ As smart speakers like the Amazon Echo become more popular, they have given rise to rampant concerns regarding user privacy. This work investigates machine
learning techniques to extract ostensibly private information from the TCP traffic moving between an Echo device and Amazon's servers, despite the fact that all such traffic is encrypted. Specifically, we investigate two
supervised classification problems using six machine
learning algorithms and three feature vectors. The "request type classification" problem seeks to determine what type of user request is being answered by the Echo. With six classes, we achieve 97% accuracy in this task using random forests. The "speaker identification" problem seeks to determine who, of a finite set of possible speakers, is speaking to the Echo. In this task, with two classes, we outperform random guessing by a small but statistically significant margin with an accuracy of 58%. We discuss the reasons for, and implications of, these results, and suggest several avenues for future research in this domain.
Advisors/Committee Members: Camp, Tracy (advisor), Wang, Hua (committee member), Schurgot, Mary (committee member).
Subjects/Keywords: Supervised classification; Machine learning
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Jackson, R. B. (2018). Machine learning for encrypted Amazon Echo traffic classification. (Masters Thesis). Colorado School of Mines. Retrieved from http://hdl.handle.net/11124/172223
Chicago Manual of Style (16th Edition):
Jackson, Ryan Blake. “Machine learning for encrypted Amazon Echo traffic classification.” 2018. Masters Thesis, Colorado School of Mines. Accessed November 20, 2019.
http://hdl.handle.net/11124/172223.
MLA Handbook (7th Edition):
Jackson, Ryan Blake. “Machine learning for encrypted Amazon Echo traffic classification.” 2018. Web. 20 Nov 2019.
Vancouver:
Jackson RB. Machine learning for encrypted Amazon Echo traffic classification. [Internet] [Masters thesis]. Colorado School of Mines; 2018. [cited 2019 Nov 20].
Available from: http://hdl.handle.net/11124/172223.
Council of Science Editors:
Jackson RB. Machine learning for encrypted Amazon Echo traffic classification. [Masters Thesis]. Colorado School of Mines; 2018. Available from: http://hdl.handle.net/11124/172223
University of Adelaide
8.
Shen, Tong.
Context Learning and Weakly Supervised Learning for Semantic Segmentation.
Degree: 2018, University of Adelaide
URL: http://hdl.handle.net/2440/120354
► This thesis focuses on one of the fundamental problems in computer vision, semantic segmentation, whose task is to predict a semantic label for each pixel…
(more)
▼ This thesis focuses on one of the fundamental problems in computer vision, semantic segmentation, whose task is to predict a semantic label for each pixel of an image. Although semantic segmentation models have been largely improved thanks to the great representative power of deep
learning techniques, there are still open questions needed to be discussed. In this thesis, we discuss two problems regarding semantic segmentation, scene consistency and weakly
supervised segmentation. In the first part of the thesis, we discuss the issue of scene consistency in semantic segmentation. This issue comes from the fact that trained models sometimes produce noisy and implausible predictions that are not semantically consistent with the scene or context. By explicitly considering scene consistency both locally and globally, we can narrow down the possible categories for each pixel and generate the desired prediction more easily. In the thesis, we address this issue by introducing a dense multi-label module. In general, multi-label classification refers to the task of assigning multiple labels to a given image. We extend the idea to different levels of the image, and assign multiple labels to different regions of the image. Dense multi-label acts as a constraint to encourage scene consistency locally and globally. For dense prediction problems such as semantic segmentation, training a model requires densely annotated data as ground-truth, which involves a great amount of human annotation effort and is very time-consuming. Therefore, it is worth investigating semi- or weakly
supervised methods that require much less supervision. Particularly, weakly
supervised segmentation refers to training the model using only image-level labels, while semi-
supervised segmentation refers to using partially annotated data or a small portion of fully annotated data to train. In the thesis, two weakly
supervised methods are proposed where only image-level labels are required. The two methods share some similar motivations. First of all, since pixel-level masks are missing in this particular setting, the two methods are all designed to estimate the missing ground-truth and further use them as pseudo ground-truth for training. Secondly, they both use data retrieved from the internet as auxiliary data because web data are cheap to obtain and exist in a large amount. Although there are similarities between these two methods, they are designed from different perspectives. The motivation for the first method is that given a group of images crawled from the internet that belong to the same semantic category, it is a good choice to use co-segmentation to extract the masks of them, which gives us almost free pixel-wise training samples. Those internet images along with the extracted masks are used to train a mask generator to help us estimate the pseudo ground-truth for the training images. The second method is designed as a bi-directional framework between the target domain and the web domain. The term “bi-directional” refers to the concept that the…
Advisors/Committee Members: Shen, Chunhua (advisor), School of Computer Science (school).
Subjects/Keywords: weakly supervised learning; semantic segmentation
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Shen, T. (2018). Context Learning and Weakly Supervised Learning for Semantic Segmentation. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/120354
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Shen, Tong. “Context Learning and Weakly Supervised Learning for Semantic Segmentation.” 2018. Thesis, University of Adelaide. Accessed November 20, 2019.
http://hdl.handle.net/2440/120354.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Shen, Tong. “Context Learning and Weakly Supervised Learning for Semantic Segmentation.” 2018. Web. 20 Nov 2019.
Vancouver:
Shen T. Context Learning and Weakly Supervised Learning for Semantic Segmentation. [Internet] [Thesis]. University of Adelaide; 2018. [cited 2019 Nov 20].
Available from: http://hdl.handle.net/2440/120354.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Shen T. Context Learning and Weakly Supervised Learning for Semantic Segmentation. [Thesis]. University of Adelaide; 2018. Available from: http://hdl.handle.net/2440/120354
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Sydney
9.
He, Fengxiang.
Instance-Dependent Positive-Unlabelled Learning
.
Degree: 2018, University of Sydney
URL: http://hdl.handle.net/2123/20115
► An emerging topic in machine learning is how to learn classifiers from datasets containing only positive and unlabelled examples (PU learning). This problem has significant…
(more)
▼ An emerging topic in machine learning is how to learn classifiers from datasets containing only positive and unlabelled examples (PU learning). This problem has significant importance in both academia and industry.
This thesis addresses the PU learning problem following a natural strategy that treats unlabelled data as negative. By this way, a PU dataset is transferred to a fully-labelled dataset but with label noise. This strategy has been employed by many existing works and is usually called the one-side noise model.
Under the framework of the one-side noise model, this thesis proposes an instance-dependent model to express how likely a negative label is corrupted. The model relies on the probabilistic gap, which is defined as the difference between the posteriors that an instance is respectively from the classes of positive or negative. Intuitively, the instance with a smaller probabilistic gap is more likely to be wrongly labelled. Motivated by this intuition, this thesis assumes there is a negative correlation between the noisy probability of the instance and the corresponding probabilistic gap. This model is named as probabilistic-gap PU model (PGPU model).
Based on the PGPU model, this thesis designs Bayesian relabelling method that can select a group of the unlabelled instances and give them new labels that are identical to the ones assigned by a Bayesian optimal classifier. By this way, we can significantly extend the labelled dataset.
Eventually, this thesis employs conventional binary classification methods to learn a classifier from the extended labelled datasets. It is worth noting that there could be a sub-domain of the instances where no data point can be relabelled. This issue could lead to a biased classifier. A kernel mean matching technique is then employed to remedy this problem.
This thesis also evaluates the proposed method in both theoretical and empirical manners. Both theoretical and empirical results are in agreements with our method.
Subjects/Keywords: Postive-unlabelled learning;
weakly supervised learning
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
He, F. (2018). Instance-Dependent Positive-Unlabelled Learning
. (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/20115
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
He, Fengxiang. “Instance-Dependent Positive-Unlabelled Learning
.” 2018. Thesis, University of Sydney. Accessed November 20, 2019.
http://hdl.handle.net/2123/20115.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
He, Fengxiang. “Instance-Dependent Positive-Unlabelled Learning
.” 2018. Web. 20 Nov 2019.
Vancouver:
He F. Instance-Dependent Positive-Unlabelled Learning
. [Internet] [Thesis]. University of Sydney; 2018. [cited 2019 Nov 20].
Available from: http://hdl.handle.net/2123/20115.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
He F. Instance-Dependent Positive-Unlabelled Learning
. [Thesis]. University of Sydney; 2018. Available from: http://hdl.handle.net/2123/20115
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Texas – Austin
10.
Joshi, Shalmali Dilip.
Constraint based approaches to interpretable and semi-supervised machine learning.
Degree: PhD, Electrical and Computer Engineering, 2019, University of Texas – Austin
URL: http://dx.doi.org/10.26153/tsw/1259
► Interpretability and Explainability of machine learning algorithms are becoming increasingly important as Machine Learning (ML) systems get widely applied to domains like clinical healthcare, social…
(more)
▼ Interpretability and Explainability of machine
learning algorithms are becoming increasingly important as Machine
Learning (ML) systems get widely applied to domains like clinical healthcare, social media and governance. A related major challenge in deploying ML systems pertains to reliable
learning when expert annotation is severely limited. This dissertation prescribes a common framework to address these challenges, based on the use of constraints that can make an ML model more interpretable, lead to novel methods for explaining ML models, or help to learn reliably with limited supervision.
In particular, we focus on the class of latent variable models and develop a general
learning framework by constraining realizations of latent variables and/or model parameters. We propose specific constraints that can be used to develop identifiable latent variable models, that in turn learn interpretable outcomes. The proposed framework is first used in Non–negative Matrix Factorization and Probabilistic Graphical Models. For both models, algorithms are proposed to incorporate such constraints with seamless and tractable augmentation of the associated
learning and inference procedures. The utility of the proposed methods is demonstrated for our working application domain – identifiable phenotyping using Electronic Health Records (EHRs). Evaluation by domain experts reveals that the proposed models are indeed more clinically relevant (and hence more interpretable) than existing counterparts. The work also demonstrates that while there may be inherent trade–offs between constraining models to encourage interpretability, the quantitative performance of downstream tasks remains competitive.
We then focus on constraint based mechanisms to explain decisions or outcomes of
supervised black-box models. We propose an explanation model based on generating examples where the nature of the examples is constrained i.e. they have to be sampled from the underlying data domain. To do so, we train a generative model to characterize the data manifold in a high dimensional ambient space. Constrained sampling then allows us to generate naturalistic examples that lie along the data manifold. We propose ways to summarize model behavior using such constrained examples.
In the last part of the contributions, we argue that heterogeneity of data sources is useful in situations where very little to no supervision is available. This thesis leverages such heterogeneity (via constraints) for two critical but widely different machine
learning algorithms. In each case, a novel algorithm in the sub-class of co–regularization is developed to combine information from heterogeneous sources. Co–regularization is a framework of constraining latent variables and/or latent distributions in order to leverage heterogeneity. The proposed algorithms are utilized for clustering, where the intent is to generate a partition or grouping of observed samples, and for
Learning to Rank algorithms – used to rank a set of observed samples in order of preference with respect…
Advisors/Committee Members: Ghosh, Joydeep (advisor), Koyejo, Oluwasanmi (committee member), Sanghavi, Sujay (committee member), Vikalo, Haris (committee member), Mooney, Raymond (committee member).
Subjects/Keywords: Interpretable machine learning; Semi-supervised machine learning
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Joshi, S. D. (2019). Constraint based approaches to interpretable and semi-supervised machine learning. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/1259
Chicago Manual of Style (16th Edition):
Joshi, Shalmali Dilip. “Constraint based approaches to interpretable and semi-supervised machine learning.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed November 20, 2019.
http://dx.doi.org/10.26153/tsw/1259.
MLA Handbook (7th Edition):
Joshi, Shalmali Dilip. “Constraint based approaches to interpretable and semi-supervised machine learning.” 2019. Web. 20 Nov 2019.
Vancouver:
Joshi SD. Constraint based approaches to interpretable and semi-supervised machine learning. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2019 Nov 20].
Available from: http://dx.doi.org/10.26153/tsw/1259.
Council of Science Editors:
Joshi SD. Constraint based approaches to interpretable and semi-supervised machine learning. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/1259
University of Western Ontario
11.
Ao, Shuang.
Visual Transfer Learning in the Absence of the Source Data.
Degree: 2017, University of Western Ontario
URL: https://ir.lib.uwo.ca/etd/4463
► Image recognition has become one of the most popular topics in machine learning. With the development of Deep Convolutional Neural Networks (CNN) and the help…
(more)
▼ Image recognition has become one of the most popular topics in machine learning. With the development of Deep Convolutional Neural Networks (CNN) and the help of the large scale labeled image database such as ImageNet, modern image recognition models can achieve competitive performance compared to human annotation in some general image recognition tasks. Many IT companies have adopted it to improve their visual related tasks. However, training these large scale deep neural networks requires thousands or even millions of labeled images, which is an obstacle when applying it to a specific visual task with limited training data. Visual transfer learning is proposed to solve this problem. Visual transfer learning aims at transferring the knowledge from a source visual task to a target visual task. Typically, the target task is related to the source task, and the training data in the target task is relatively small. In visual transfer learning, the majority of existing methods assume that the source data is freely available and use the source data to measure the discrepancy between the source and target task to help the transfer process. However, in many real applications, source data are often a subject of legal, technical and contractual constraints between data owners and data customers. Beyond privacy and disclosure obligations, customers are often reluctant to share their data. When operating customer care, collected data may include information on recent technical problems which is a highly sensitive topic that companies are not willing to share. This scenario is often called Hypothesis Transfer Learning (HTL) where the source data is absent. Therefore, these previous methods cannot be applied to many real visual transfer learning problems. In this thesis, we investigate the visual transfer learning problem under HTL setting. Instead of using the source data to measure the discrepancy, we use the source model as the proxy to transfer the knowledge from the source task to the target task. Compared to the source data, the well-trained source model is usually freely accessible in many tasks and contains equivalent source knowledge as well. Specifically, in this thesis, we investigate the visual transfer learning in two scenarios: domain adaptation and learning new categories. In contrast to the previous methods in HTL, our methods can both leverage knowledge from more types of source models and achieve better transfer performance. In chapter 3, we investigate the visual domain adaptation problem under the setting of Hypothesis Transfer Learning. We propose Effective Multiclass Transfer Learning (EMTLe) that can effectively transfer the knowledge when the size of the target set is small. Specifically, EMTLe can effectively transfer the knowledge using the outputs of the source models as the auxiliary bias to adjust the prediction in the target task. Experiment results show that EMTLe can outperform other baselines under the setting of HTL. In chapter 4, we investigate the semi-supervised domain adaptation scenario under…
Subjects/Keywords: Visual Transfer Learning; Hypothesis Transfer Learning; Supervised Learning; Semi-supervised Learning; Artificial Intelligence and Robotics
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Ao, S. (2017). Visual Transfer Learning in the Absence of the Source Data. (Thesis). University of Western Ontario. Retrieved from https://ir.lib.uwo.ca/etd/4463
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Ao, Shuang. “Visual Transfer Learning in the Absence of the Source Data.” 2017. Thesis, University of Western Ontario. Accessed November 20, 2019.
https://ir.lib.uwo.ca/etd/4463.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Ao, Shuang. “Visual Transfer Learning in the Absence of the Source Data.” 2017. Web. 20 Nov 2019.
Vancouver:
Ao S. Visual Transfer Learning in the Absence of the Source Data. [Internet] [Thesis]. University of Western Ontario; 2017. [cited 2019 Nov 20].
Available from: https://ir.lib.uwo.ca/etd/4463.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Ao S. Visual Transfer Learning in the Absence of the Source Data. [Thesis]. University of Western Ontario; 2017. Available from: https://ir.lib.uwo.ca/etd/4463
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
12.
Byun, Byungki.
On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling.
Degree: PhD, Electrical and Computer Engineering, 2012, Georgia Tech
URL: http://hdl.handle.net/1853/43597
► This dissertation presents the development of a semi-supervised incremental learning framework with a multi-view perspective for image concept modeling. For reliable image concept characterization, having…
(more)
▼ This dissertation presents the development of a semi-
supervised incremental
learning framework with a multi-view perspective for image concept modeling. For reliable image concept characterization, having a large number of labeled images is crucial. However, the size of the training set is often limited due to the cost required for generating concept labels associated with objects in a large quantity of images. To address this issue, in this research, we propose to incrementally incorporate unlabeled samples into a
learning process to enhance concept models originally learned with a small number of labeled samples. To tackle the sub-optimality problem of conventional techniques, the proposed incremental
learning framework selects unlabeled samples based on an expected error reduction function that measures contributions of the unlabeled samples based on their ability to increase the modeling accuracy. To improve the convergence property of the proposed incremental
learning framework, we further propose a multi-view
learning approach that makes use of multiple features such as color, texture, etc., of images when including unlabeled samples. For robustness to mismatches between training and testing conditions, a discriminative
learning algorithm, namely a kernelized maximal- figure-of-merit (kMFoM)
learning approach is also developed. Combining individual techniques, we conduct a set of experiments on various image concept modeling problems, such as handwritten digit recognition, object recognition, and image spam detection to highlight the effectiveness of the proposed framework.
Advisors/Committee Members: Lee, Chin-Hui (Committee Chair), Clements, Mark (Committee Member), Lee, Hsien-Hsin (Committee Member), McClellan, James (Committee Member), Yuan, Ming (Committee Member).
Subjects/Keywords: Discriminative learning; Semi-supervised learning; Incremental learning; Image modeling; Multi-view learning; Machine learning; Supervised learning (Machine learning); Boosting (Algorithms)
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Byun, B. (2012). On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/43597
Chicago Manual of Style (16th Edition):
Byun, Byungki. “On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling.” 2012. Doctoral Dissertation, Georgia Tech. Accessed November 20, 2019.
http://hdl.handle.net/1853/43597.
MLA Handbook (7th Edition):
Byun, Byungki. “On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling.” 2012. Web. 20 Nov 2019.
Vancouver:
Byun B. On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2019 Nov 20].
Available from: http://hdl.handle.net/1853/43597.
Council of Science Editors:
Byun B. On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/43597
University of Alberta
13.
Mahmood, Ashique.
Automatic step-size adaptation in incremental supervised
learning.
Degree: MS, Department of Computing Science, 2010, University of Alberta
URL: https://era.library.ualberta.ca/files/zc77sr03r
► Performance and stability of many iterative algorithms such as stochastic gradient descent largely depend on a fixed and scalar step-size parameter. Use of a fixed…
(more)
▼ Performance and stability of many iterative algorithms
such as stochastic gradient descent largely depend on a fixed and
scalar step-size parameter. Use of a fixed and scalar step-size
value may lead to limited performance in many problems. We study
several existing step-size adaptation algorithms in nonstationary,
supervised learning problems using simulated and real-world data.
We discover that effectiveness of the existing step-size adaptation
algorithms requires tuning of a meta parameter across problems. We
introduce a new algorithm - Autostep - by combining several new
techniques with an existing algorithm, and demonstrate that it can
effectively adapt a vector step-size parameter on all of our
training and test problems without tuning its meta parameter across
them. Autostep is the first step-size adaptation algorithm that can
be used in widely different problems with the same setting of all
of its parameters.
Subjects/Keywords: step size; supervised learning; stochastic gradient descent
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Mahmood, A. (2010). Automatic step-size adaptation in incremental supervised
learning. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/zc77sr03r
Chicago Manual of Style (16th Edition):
Mahmood, Ashique. “Automatic step-size adaptation in incremental supervised
learning.” 2010. Masters Thesis, University of Alberta. Accessed November 20, 2019.
https://era.library.ualberta.ca/files/zc77sr03r.
MLA Handbook (7th Edition):
Mahmood, Ashique. “Automatic step-size adaptation in incremental supervised
learning.” 2010. Web. 20 Nov 2019.
Vancouver:
Mahmood A. Automatic step-size adaptation in incremental supervised
learning. [Internet] [Masters thesis]. University of Alberta; 2010. [cited 2019 Nov 20].
Available from: https://era.library.ualberta.ca/files/zc77sr03r.
Council of Science Editors:
Mahmood A. Automatic step-size adaptation in incremental supervised
learning. [Masters Thesis]. University of Alberta; 2010. Available from: https://era.library.ualberta.ca/files/zc77sr03r
Baylor University
14.
[No author].
Semi-supervised learning for electrocardiography signal classification.
Degree: 2018, Baylor University
URL: http://hdl.handle.net/2104/10391
► An electrocardiogram (ECG) is a cardiology test that provides information about the structure and function of the heart. The size of the ECG data collected…
(more)
▼ An electrocardiogram (ECG) is a cardiology test that provides information about the structure and function of the heart. The size of the ECG data collected from patients can be very large, and the data analysis is tedious. Inspired by human
learning, in this thesis we propose a new semi-
supervised training framework for deep neural network to classify ECG data. The idea is to reward the valid associations that belong to the same class after a round trip during cross-matching of
supervised and unsupervised
learning, while penalizing the incorrect associations. The implementation of our framework can be easily integrated with any existing training setup. With data preprocessing, the detection of heart disease is improved.
Advisors/Committee Members: Dong, Liang, 1974- (advisor).
Subjects/Keywords: Semi-supervised learning; Electrocardiography; pattern recognition
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
author], [. (2018). Semi-supervised learning for electrocardiography signal classification.
(Thesis). Baylor University. Retrieved from http://hdl.handle.net/2104/10391
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
author], [No. “Semi-supervised learning for electrocardiography signal classification.
” 2018. Thesis, Baylor University. Accessed November 20, 2019.
http://hdl.handle.net/2104/10391.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
author], [No. “Semi-supervised learning for electrocardiography signal classification.
” 2018. Web. 20 Nov 2019.
Vancouver:
author] [. Semi-supervised learning for electrocardiography signal classification.
[Internet] [Thesis]. Baylor University; 2018. [cited 2019 Nov 20].
Available from: http://hdl.handle.net/2104/10391.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
author] [. Semi-supervised learning for electrocardiography signal classification.
[Thesis]. Baylor University; 2018. Available from: http://hdl.handle.net/2104/10391
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Manchester
15.
Rostamniakankalhori, Sharareh.
Integrated Supervised and Unsupervised Learning Method to
Predict the Outcome of Tuberculosis Treatment Course.
Degree: 2011, University of Manchester
URL: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:132404
► Tuberculosis (TB) is an infectious disease which is a global public health problem with over 9 million new cases annually. Tuberculosis treatment, with patient supervision…
(more)
▼ Tuberculosis (TB) is an infectious disease which is
a global public health problem with over 9 million new cases
annually. Tuberculosis treatment, with patient supervision and
support is an element of the global plan to stop TB designed by the
World Health Organization in 2006. The plan requires prediction of
patient treatment course destination. The prediction outcome can be
used to determine how intensive the level of supplying services and
supports in frame of DOTS therapy should be. No predictive model
for the outcome has been developed yet and only limited reports of
influential factors for considered outcome are available.To fill
this gap, this thesis develops a machine
learning approach to
predict the outcome of tuberculosis treatment course, which
includes, firstly, data of 6,450 Iranian TB patients under DOTS
(directly observed treatment, short course ) therapy were analysed
to initially diagnose the significant predictors by correlation
analysis; secondly, these significant features were applied to find
the best classification approach from six examined algorithms
including decision tree, Bayesian network, logistic regression,
multilayer perceptron, radial basis function, and support vector
machine; thirdly, the prediction accuracy of these existing
techniques was improved by proposing and developing a new
integrated method of k-mean clustering and classification
algorithms. Finally, a cluster-based simplified decision tree
(CSDT) was developed through an innovative hierarchical clustering
and classification algorithm. CSDT was built by k-mean partitioning
and the decision tree
learning. This innovative method not only
improves the prediction accuracy significantly but also leads to a
much simpler and interpretative decision tree.The main results of
this study included, firstly, finding seventeen significantly
correlated features which were: age, sex, weight, nationality, area
of residency, current stay in prison, low body weight, TB type,
treatment category, length of disease, TB case type, recent TB
infection, diabetic or HIV positive, and social risk factors like
history of imprisonment, IV drug usage, and unprotected sex ;
secondly, the results by applying and comparing six applied
supervised machine
learning tools on the testing set revealed that
decision trees gave the best prediction accuracy (74.21%) compared
with other methods; thirdly, by using testing set, the new
integrated approach to combine the clustering and classification
approach leads to the prediction accuracy improvement for all
applied classifiers; the most and least improvement for prediction
accuracy were shown by logistic regression (10%) and support vector
machine (4%) respectively. Finally, by applying the proposed and
developed CSDT, cluster-based simplified decision trees were
optioned, which reduced the size of the resulting decision tree and
further improved the prediction accuracy.Data type and having
normal distribution have created an opportunity for the decision
tree to outperform other algorithms. Pre-
learning by k-mean…
Advisors/Committee Members: Zeng, Xiaojun.
Subjects/Keywords: Integrated Supervised and Unsupervised Learning;
Tuberculosis; plediction
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Rostamniakankalhori, S. (2011). Integrated Supervised and Unsupervised Learning Method to
Predict the Outcome of Tuberculosis Treatment Course. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:132404
Chicago Manual of Style (16th Edition):
Rostamniakankalhori, Sharareh. “Integrated Supervised and Unsupervised Learning Method to
Predict the Outcome of Tuberculosis Treatment Course.” 2011. Doctoral Dissertation, University of Manchester. Accessed November 20, 2019.
http://www.manchester.ac.uk/escholar/uk-ac-man-scw:132404.
MLA Handbook (7th Edition):
Rostamniakankalhori, Sharareh. “Integrated Supervised and Unsupervised Learning Method to
Predict the Outcome of Tuberculosis Treatment Course.” 2011. Web. 20 Nov 2019.
Vancouver:
Rostamniakankalhori S. Integrated Supervised and Unsupervised Learning Method to
Predict the Outcome of Tuberculosis Treatment Course. [Internet] [Doctoral dissertation]. University of Manchester; 2011. [cited 2019 Nov 20].
Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:132404.
Council of Science Editors:
Rostamniakankalhori S. Integrated Supervised and Unsupervised Learning Method to
Predict the Outcome of Tuberculosis Treatment Course. [Doctoral Dissertation]. University of Manchester; 2011. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:132404
McMaster University
16.
Ateeq, Sameen.
Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries.
Degree: MSc, 2018, McMaster University
URL: http://hdl.handle.net/11375/24095
► According to the Public Health Agency of Canada, falls account for 95% of all hip fractures in Canada; 20% of fall-related injury cases end in…
(more)
▼ According to the Public Health Agency of Canada, falls account for 95% of all hip fractures in Canada; 20% of fall-related injury cases end in death. This thesis evaluates the predictive power of many variables to predict fall-related injuries. The dataset chosen was CCHS which is high dimensional and diverse. The use of Principal Component Analysis (PCA) and random forest was employed to determine the highest priority risk factors to include in the predictive model. The results show that it is possible to predict fall-related injuries with a sensitivity of 80% or higher using four predictors (frequency of consultations with medical doctor, food and vegetable consumption, height and monthly physical activity level of over 15 minutes). Alternatively, the same sensitivity can be reached using age, frequency of walking for exercise per 3 months, alcohol consumption and personal income. None of the predictive models reached an accuracy of 70% or higher.
Further work in studying nutritional diets that offer protection from incurring a fall related injury are also recommended. Since the predictors are behavioral determinants of health and have a high sensitivity but a low accuracy, population health interventions are recommended rather than individual-level interventions. Suggestions to improve accuracy of built models are also proposed.
Thesis
Master of Science (MSc)
Advisors/Committee Members: Samavi, Reza, eHealth.
Subjects/Keywords: machine learning; supervised classification; falls; CCHS; injuries
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Ateeq, S. (2018). Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/24095
Chicago Manual of Style (16th Edition):
Ateeq, Sameen. “Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries.” 2018. Masters Thesis, McMaster University. Accessed November 20, 2019.
http://hdl.handle.net/11375/24095.
MLA Handbook (7th Edition):
Ateeq, Sameen. “Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries.” 2018. Web. 20 Nov 2019.
Vancouver:
Ateeq S. Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries. [Internet] [Masters thesis]. McMaster University; 2018. [cited 2019 Nov 20].
Available from: http://hdl.handle.net/11375/24095.
Council of Science Editors:
Ateeq S. Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries. [Masters Thesis]. McMaster University; 2018. Available from: http://hdl.handle.net/11375/24095
Halmstad University
17.
Xiang, Gao.
Supervised Methods for Fault Detection in Vehicle.
Degree: Intelligent systems (IS-lab), 2010, Halmstad University
URL: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-14151
► Uptime and maintenance planning are important issues for vehicle operators (e.g.operators of bus fleets). Unplanned downtime can cause a bus operator to be fined…
(more)
▼ Uptime and maintenance planning are important issues for vehicle operators (e.g.operators of bus fleets). Unplanned downtime can cause a bus operator to be fined if the vehicle is not on time. Supervised classification methods for detecting faults in vehicles are compared in this thesis. Data has been collected by a vehicle manufacturer including three kinds of faulty states in vehicles (i.e. charge air cooler leakage, radiator and air filter clogging). The problem consists of differentiating between the normal data and the three different categories of faulty data. Evaluated methods include linear model, neural networks model, 1-nearest neighbor and random forest model. For every kind of model, a variable selection method should be used. In our thesis we try to find the best model for this problem, and also select the most important input signals. After we compare these four models, we found that the best accuracy (96.9% correct classifications) was achieved with the random forest model.
Subjects/Keywords: fault detection; Supervised Methods; Machine Learning
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Xiang, G. (2010). Supervised Methods for Fault Detection in Vehicle. (Thesis). Halmstad University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-14151
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Xiang, Gao. “Supervised Methods for Fault Detection in Vehicle.” 2010. Thesis, Halmstad University. Accessed November 20, 2019.
http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-14151.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Xiang, Gao. “Supervised Methods for Fault Detection in Vehicle.” 2010. Web. 20 Nov 2019.
Vancouver:
Xiang G. Supervised Methods for Fault Detection in Vehicle. [Internet] [Thesis]. Halmstad University; 2010. [cited 2019 Nov 20].
Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-14151.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Xiang G. Supervised Methods for Fault Detection in Vehicle. [Thesis]. Halmstad University; 2010. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-14151
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
18.
Zhao, Xuran.
Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics.
Degree: Docteur es, Signal et images, 2013, Paris, ENST
URL: http://www.theses.fr/2013ENST0061
► Dans la plupart des systèmes biométriques de l’état de l’art, les données biométrique sont souvent représentés par des vecteurs de grande dimensionalité. La dimensionnalité d'éléments…
(more)
▼ Dans la plupart des systèmes biométriques de l’état de l’art, les données biométrique sont souvent représentés par des vecteurs de grande dimensionalité. La dimensionnalité d'éléments biométriques génèrent un problème de malédiction de dimensionnalité. Dans la biométrie multimodale, différentes modalités biométriques peuvent former différents entrés des algorithmes de classification. La fusion des modalités reste un problème difficile et est généralement traitée de manière isolée à celui de dimensionalité élevée. Cette thèse aborde le problème de la dimensionnalité élevée et le problème de la fusion multimodale dans un cadre unifié. En vertu d'un paramètre biométrique multi-modale et les données non étiquetées abondantes données, nous cherchons à extraire des caractéristiques discriminatoires de multiples modalités d'une manière non supervisée. Les contributions de cette thèse sont les suivantes: Un état de l’art des algorithmes RMVD de l'état de l'art ; Un nouveau concept de RMVD: accord de la structure de données dans sous-espace; Trois nouveaux algorithmes de MVDR basée sur des définitions différentes de l’accord de la structure dans les sous-espace; L’application des algorithmes proposés à la classification semi-supervisée, la classification non supervisée, et les problèmes de récupération de données biométriques, en particulier dans un contexte de la reconnaissance de personne en audio et vidéo; L’application des algorithmes proposés à des problèmes plus larges de reconnaissance des formes pour les données non biométriques, tels que l'image et le regroupement de texte et la recherche.
Biometric data is often represented by high-dimensional feature vectors which contain significant inter-session variation. Discriminative dimensionality reduction techniques generally follow a supervised learning scheme. However, labelled training data is generally limited in quantity and often does not reliably represent the inter-session variation encountered in test data. This thesis proposes to use multi-view dimensionality reduction (MVDR) which aims to extract discriminative features in multi-modal biometric systems, where different modalities are regarded as different views of the same data. MVDR projections are trained on feature-feature pairs where label information is not required. Since unlabelled data is easier to acquire in large quantities, and because of the natural co-existence of multiple views in multi-modal biometric problems, discriminant, low-dimensional subspaces can be learnt using the proposed MVDR approaches in a largely unsupervised manner. According to different functionalities of biometric systems, namely, clustering, and retrieval, we propose three MVDR frameworks which meet the requirements for each functionality. The proposed approaches, however, share the same spirit: all methods aim to learn a projection for each view such that a certain form of agreement is attained in the subspaces across different views. The proposed MVDR frameworks can thus be unified into one general framework for multi-view…
Advisors/Committee Members: Evans, Nicholas W. D. (thesis director), Dugelay, Jean-Luc (thesis director).
Subjects/Keywords: Apprentissage semi-supervisé; Semi-supervised learning
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Zhao, X. (2013). Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics. (Doctoral Dissertation). Paris, ENST. Retrieved from http://www.theses.fr/2013ENST0061
Chicago Manual of Style (16th Edition):
Zhao, Xuran. “Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics.” 2013. Doctoral Dissertation, Paris, ENST. Accessed November 20, 2019.
http://www.theses.fr/2013ENST0061.
MLA Handbook (7th Edition):
Zhao, Xuran. “Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics.” 2013. Web. 20 Nov 2019.
Vancouver:
Zhao X. Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics. [Internet] [Doctoral dissertation]. Paris, ENST; 2013. [cited 2019 Nov 20].
Available from: http://www.theses.fr/2013ENST0061.
Council of Science Editors:
Zhao X. Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics. [Doctoral Dissertation]. Paris, ENST; 2013. Available from: http://www.theses.fr/2013ENST0061
Georgia Tech
19.
Ahsan, Unaiza.
Leveraging Mid-level Representations for Complex Activity Recognition.
Degree: PhD, Interactive Computing, 2019, Georgia Tech
URL: http://hdl.handle.net/1853/61199
► Dynamic scene understanding requires learning representations of the components of the scene including objects, environments, actions and events. Complex activity recognition from images and videos…
(more)
▼ Dynamic scene understanding requires
learning representations of the components of the scene including objects, environments, actions and events. Complex activity recognition
from images and videos requires annotating large datasets with action labels which is a tedious and expensive task. Thus, there is a need to design a mid-level or intermediate
feature representation which does not require millions of labels, yet is able to generalize to semantic-level recognition of activities in visual data. This thesis makes three contributions
in this regard. First, we propose an event concept-based intermediate representation which learns concepts via the Web and uses this representation to identify events even with a single labeled example. To demonstrate the strength of the proposed approaches, we contribute two diverse social event datasets to the community. We then present a use case of event concepts as a mid-level representation that generalizes to sentiment recognition in diverse social
event images. Second, we propose to train Generative Adversarial Networks (GANs) with video frames (which does not require labels), use the trained discriminator from GANs as an
intermediate representation and finetune it on a smaller labeled video activity dataset to recognize actions in videos. This unsupervised pre-training step avoids any manual feature
engineering, video frame encoding or searching for the best video frame sampling technique. Our third contribution is a self-
supervised learning approach on videos that exploits both spatial and temporal coherency to learn feature representations on video data without
any supervision. We demonstrate the transfer
learning capability of this model on smaller labeled datasets. We present comprehensive experimental analysis on the self-
supervised
model to provide insights into the unsupervised pretraining paradigm and how it can help with activity recognition on target datasets which the model has never seen during training.
Advisors/Committee Members: Essa, Irfan (advisor), Hays, James (committee member), De Choudhury, Munmun (committee member), Kira, Zsolt (committee member), Parikh, Devi (committee member), Sun, Chen (committee member).
Subjects/Keywords: activity recognition; self-supervised learning; event recognition
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Ahsan, U. (2019). Leveraging Mid-level Representations for Complex Activity Recognition. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61199
Chicago Manual of Style (16th Edition):
Ahsan, Unaiza. “Leveraging Mid-level Representations for Complex Activity Recognition.” 2019. Doctoral Dissertation, Georgia Tech. Accessed November 20, 2019.
http://hdl.handle.net/1853/61199.
MLA Handbook (7th Edition):
Ahsan, Unaiza. “Leveraging Mid-level Representations for Complex Activity Recognition.” 2019. Web. 20 Nov 2019.
Vancouver:
Ahsan U. Leveraging Mid-level Representations for Complex Activity Recognition. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2019 Nov 20].
Available from: http://hdl.handle.net/1853/61199.
Council of Science Editors:
Ahsan U. Leveraging Mid-level Representations for Complex Activity Recognition. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61199
University of Notre Dame
20.
Troy William Raeder.
Evaluating and Maintaining Classification
Algorithms</h1>.
Degree: PhD, Computer Science and Engineering, 2012, University of Notre Dame
URL: https://curate.nd.edu/show/4b29b56616h
► Any practical application of machine learning necessarily begins with the selection of a classification algorithm. Generally, practitioners will try several different types of algorithms…
(more)
▼ Any practical application of machine
learning
necessarily begins with the selection of a classification
algorithm. Generally, practitioners will try several different
types of algorithms (such as decision trees, Bayesian algorithms,
support vector machines, or neural networks) and select the
algorithm that performs best on a subset of the available data.
That is to say, some measurement of the classifier’s performance on
past data is used as an estimate of its performance on future data.
Ideally, this estimate is perfectly aligned with the extit{true
cost} of applying the classifier on future data, but this far from
guaranteed in practice. First, any estimate of classifier
performance has variance, and this variance is difficult to
estimate. Additionally, misclassification costs are rarely known at
model-selection time and the characteristics of the population from
which data are drawn may change over time. If the training-time
estimate of either misclassification cost or data distribution is
incorrect, the chosen classifier is sub-optimal and may perform
worse than expected. Finally, once a suitable classifier is built
and deployed, there need to be systems in place to ensure that it
continues to perform at a high level over time. The purpose of this
dissertation is to improve the processes of classifier evaluation,
selection, and maintenance in real-world
situations.
Advisors/Committee Members: Dr. W. Philip Kegelmeyer, Committee Member, Dr. Patrick J. Flynn, Committee Member, Dr. Nitesh V. Chawla, Committee Chair, Dr. Kevin W. Bowyer, Committee Member.
Subjects/Keywords: classification; supervised learning; evaluation; concept drift
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Raeder, T. W. (2012). Evaluating and Maintaining Classification
Algorithms</h1>. (Doctoral Dissertation). University of Notre Dame. Retrieved from https://curate.nd.edu/show/4b29b56616h
Chicago Manual of Style (16th Edition):
Raeder, Troy William. “Evaluating and Maintaining Classification
Algorithms</h1>.” 2012. Doctoral Dissertation, University of Notre Dame. Accessed November 20, 2019.
https://curate.nd.edu/show/4b29b56616h.
MLA Handbook (7th Edition):
Raeder, Troy William. “Evaluating and Maintaining Classification
Algorithms</h1>.” 2012. Web. 20 Nov 2019.
Vancouver:
Raeder TW. Evaluating and Maintaining Classification
Algorithms</h1>. [Internet] [Doctoral dissertation]. University of Notre Dame; 2012. [cited 2019 Nov 20].
Available from: https://curate.nd.edu/show/4b29b56616h.
Council of Science Editors:
Raeder TW. Evaluating and Maintaining Classification
Algorithms</h1>. [Doctoral Dissertation]. University of Notre Dame; 2012. Available from: https://curate.nd.edu/show/4b29b56616h
University of North Texas
21.
Dandala, Bharath.
Multilingual Word Sense Disambiguation Using Wikipedia.
Degree: 2013, University of North Texas
URL: https://digital.library.unt.edu/ark:/67531/metadc500036/
► Ambiguity is inherent to human language. In particular, word sense ambiguity is prevalent in all natural languages, with a large number of the words in…
(more)
▼ Ambiguity is inherent to human language. In particular, word sense ambiguity is prevalent in all natural languages, with a large number of the words in any given language carrying more than one meaning. Word sense disambiguation is the task of automatically assigning the most appropriate meaning to a polysemous word within a given context. Generally the problem of resolving ambiguity in literature has revolved around the famous quote “you shall know the meaning of the word by the company it keeps.” In this thesis, we investigate the role of context for resolving ambiguity through three different approaches. Instead of using a predefined monolingual sense inventory such as WordNet, we use a language-independent framework where the word senses and sense-tagged data are derived automatically from Wikipedia. Using Wikipedia as a source of sense-annotations provides the much needed solution for knowledge acquisition bottleneck. In order to evaluate the viability of Wikipedia based sense-annotations, we cast the task of disambiguating polysemous nouns as a monolingual classification task and experimented on lexical samples from four different languages (viz. English, German, Italian and Spanish). The experiments confirm that the Wikipedia based sense annotations are reliable and can be used to construct accurate monolingual sense classifiers. It is a long belief that exploiting multiple languages helps in building accurate word sense disambiguation systems. Subsequently, we developed two approaches that recast the task of disambiguating polysemous nouns as a multilingual classification task. The first approach for multilingual word sense disambiguation attempts to effectively use a machine translation system to leverage two relevant multilingual aspects of the semantics of text. First, the various senses of a target word may be translated into different words, which constitute unique, yet highly salient signal that effectively expand the target word’s feature space. Second, the translated context words themselves embed co-occurrence information that a translation engine gathers from very large parallel corpora. The second approach for multlingual word sense disambiguation attempts to reduce the reliance on the machine translation system during training by using the multilingual knowledge available in Wikipedia through its interlingual links. Finally, the experiments on a lexical sample from four different languages confirm that the multilingual systems perform better than the monolingual system and significantly improve the disambiguation accuracy.
Advisors/Committee Members: Mihalcea, Rada, Tarau, Paul, Nielsen, Rodney, Bunescu, Razvan.
Subjects/Keywords: Wikipedia; word sense disambiguation; supervised learning; multilingual
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
King Abdullah University of Science and Technology
22.
Xu, Mengmeng.
Object Detection Using Multiple Level Annotations.
Degree: 2019, King Abdullah University of Science and Technology
URL: http://hdl.handle.net/10754/631958
► Object detection is a fundamental problem in computer vision. Impressive results have been achieved on large-scale detection benchmarks by fully-supervised object detection (FSOD) methods. However,…
(more)
▼ Object detection is a fundamental problem in computer vision. Impressive results have been achieved on large-scale detection benchmarks by fully-supervised object detection (FSOD) methods. However, FSOD approaches require tremendous instance-level annotations, which are time-consuming to collect. In contrast, weakly supervised object detection (WSOD) exploits easily-collected image-level labels while it suffers from relatively inferior detection performance.
This thesis studies hybrid learning methods on the object detection problems. We intend to train an object detector from a dataset where both instance-level and image-level labels are employed. Extensive experiments on the challenging PASCAL VOC 2007 and 2012 benchmarks strongly demonstrate the effectiveness of our method, which gives a trade-off between collecting fewer annotations and building a more accurate object detector. Our method is also a strong baseline bridging the wide gap between FSOD and WSOD performances.
Based on the hybrid learning framework, we further study the problem of object detection from a novel perspective in which the annotation budget constraints are taken into consideration. When provided with a fixed budget, we propose a strategy for building a diverse and informative dataset that can be used to optimally train a robust detector. We investigate both optimization and learning-based methods to sample which images to annotate and which level of annotations (strongly or weakly supervised) to annotate them with.
By combining an optimal image/annotation selection scheme with the hybrid supervised learning, we show that one can achieve the performance of a strongly supervised detector on PASCAL-VOC 2007 while saving 12:8% of its original annotation budget. Furthermore, when 100% of the budget is used, it surpasses this performance by 2:0 mAP percentage points.
Subjects/Keywords: Object Detection; Hybrid Supervised Learning; Training Budget
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Xu, M. (2019). Object Detection Using Multiple Level Annotations. (Thesis). King Abdullah University of Science and Technology. Retrieved from http://hdl.handle.net/10754/631958
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Xu, Mengmeng. “Object Detection Using Multiple Level Annotations.” 2019. Thesis, King Abdullah University of Science and Technology. Accessed November 20, 2019.
http://hdl.handle.net/10754/631958.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Xu, Mengmeng. “Object Detection Using Multiple Level Annotations.” 2019. Web. 20 Nov 2019.
Vancouver:
Xu M. Object Detection Using Multiple Level Annotations. [Internet] [Thesis]. King Abdullah University of Science and Technology; 2019. [cited 2019 Nov 20].
Available from: http://hdl.handle.net/10754/631958.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Xu M. Object Detection Using Multiple Level Annotations. [Thesis]. King Abdullah University of Science and Technology; 2019. Available from: http://hdl.handle.net/10754/631958
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Linköping University
23.
Alirezaie, Marjan.
Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation.
Degree: Computer and Information Science, 2011, Linköping University
URL: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086
► The present thesis addresses machine learning in a domain of naturallanguage phrases that are names of universities. It describes two approaches to this problem…
(more)
▼ The present thesis addresses machine learning in a domain of naturallanguage phrases that are names of universities. It describes two approaches to this problem and a software implementation that has made it possible to evaluate them and to compare them.
In general terms, the system's task is to learn to 'understand' the significance of the various components of a university name, such as the city or region where the university is located, the scienti c disciplines that are studied there, or the name of a famous person which may be part of the university name. A concrete test for whether the system has acquired this understanding is when it is able to compose a plausible university name given some components that should occur in the name.
In order to achieve this capability, our system learns the structure of available names of some universities in a given data set, i.e. it acquires a grammar for the microlanguage of university names. One of the challenges is that the system may encounter ambiguities due to multi meaning words. This problem is addressed using a small ontology that is created during the training phase.
Both domain knowledge and grammatical knowledge is represented using decision trees, which is an ecient method for concept learning. Besides for inductive inference, their role is to partition the data set into a hierarchical structure which is used for resolving ambiguities.
The present report also de nes some modi cations in the de nitions of parameters, for example a parameter for entropy, which enable the system to deal with cognitive uncertainties. Our method for automatic syntax acquisition, ADIOS, is an unsupervised learning method. This method is described and discussed here, including a report on the outcome of the tests using our data set.
The software that has been implemented and used in this project has been implemented in C.
Subjects/Keywords: Machine Learning; Supervised Learning; Unsupervised Learning; Computer Sciences; Datavetenskap (datalogi)
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Alirezaie, M. (2011). Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Alirezaie, Marjan. “Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation.” 2011. Thesis, Linköping University. Accessed November 20, 2019.
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Alirezaie, Marjan. “Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation.” 2011. Web. 20 Nov 2019.
Vancouver:
Alirezaie M. Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation. [Internet] [Thesis]. Linköping University; 2011. [cited 2019 Nov 20].
Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Alirezaie M. Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation. [Thesis]. Linköping University; 2011. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Oregon State University
24.
Hao, Guohua.
Efficient training and feature induction in sequential supervised learning.
Degree: PhD, Computer Science, 2009, Oregon State University
URL: http://hdl.handle.net/1957/12548
► Sequential supervised learning problems arise in many real applications. This dissertation focuses on two important research directions in sequential supervised learning: efficient training and feature…
(more)
▼ Sequential
supervised learning problems arise in many real applications. This dissertation focuses on two important research directions in sequential
supervised learning: efficient training and feature induction.
In the direction of efficient training, we study the training of conditional random fields (CRFs), which provide a flexible and powerful model for sequential
supervised learning problems. Existing training algorithms for CRFs are slow, particularly in problems with large numbers of potential input features and feature combinations. In this dissertation, we describe a new algorithm, TreeCRF, for training CRFs via gradient tree boosting. In TreeCRF, the CRF potential functions are represented as weighted sums of regression trees, which provide compact representations of feature interactions. So the algorithm does not explicitly consider the potentially large parameter space. As a result, gradient tree boosting scales linearly in the order of the Markov model and in the order of the feature interactions, rather than exponentially as in previous algorithms based on iterative scaling and gradient descent. Detailed experimental results are provided to evaluate the performance of the TreeCRF algorithm and possible extensions of this algorithm are discussed.
We also study the problem of handling missing input values in CRFs, which has been rarely discussed in the literature. Gradient tree boosting also makes it possible to use instance weighting (as in C4.5) and surrogate splitting (as in CART) to handle missing values in CRFs. Experimental studies of the effectiveness of these two methods (as well as standard imputation and indicator feature methods) show that instance weighting is the best method in most cases when feature values are missing at random.
In the direction of feature induction, we study the search-based structured
learning framework and its application to sequential
supervised learning problems. By formulating the label sequence prediction process as an incremental search process from one end of a sequence to the other, this framework is able to avoid complicated inference algorithms in the training process and thus achieves very fast training speed. However, for problems where there exist long range dependencies between the current position and future positions, at each search step, this framework is unable to exploit these dependencies to make accurate predictions. In this dissertation, a multiple-instance
learning based algorithm is proposed to automatically extract useful features from future positions as a way to discover and exploit these long range dependencies. Integrating this algorithm with maximum entropy Markov models yields promising experimental results on both synthetic data sets and real data sets that have long range dependencies in sequences.
Advisors/Committee Members: Dietterich, Thomas G. (advisor), Fern, Alan (committee member).
Subjects/Keywords: Machine Learning; Supervised learning (Machine learning) – Mathematical models
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Hao, G. (2009). Efficient training and feature induction in sequential supervised learning. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/12548
Chicago Manual of Style (16th Edition):
Hao, Guohua. “Efficient training and feature induction in sequential supervised learning.” 2009. Doctoral Dissertation, Oregon State University. Accessed November 20, 2019.
http://hdl.handle.net/1957/12548.
MLA Handbook (7th Edition):
Hao, Guohua. “Efficient training and feature induction in sequential supervised learning.” 2009. Web. 20 Nov 2019.
Vancouver:
Hao G. Efficient training and feature induction in sequential supervised learning. [Internet] [Doctoral dissertation]. Oregon State University; 2009. [cited 2019 Nov 20].
Available from: http://hdl.handle.net/1957/12548.
Council of Science Editors:
Hao G. Efficient training and feature induction in sequential supervised learning. [Doctoral Dissertation]. Oregon State University; 2009. Available from: http://hdl.handle.net/1957/12548
University of New South Wales
25.
Wang, Weihong.
A Weakly Supervised Approach for Object Detection.
Degree: Computer Science & Engineering, 2016, University of New South Wales
URL: http://handle.unsw.edu.au/1959.4/56619
;
https://unsworks.unsw.edu.au/fapi/datastream/unsworks:41053/SOURCE02?view=true
► Object detection in images and videos is an important topic in computer vision. In general, a large number of training samples are required to train…
(more)
▼ Object detection in images and videos is an important topic in computer vision. In general, a large number of training samples are required to train an object detector with decent accuracy. However, annotating training samples can be inflexible and expensive. Weakly
supervised object detection (WSOD) can solve the problem by reducing the amount of manual annotation effort in training images. In this thesis, we propose our own WSOD approach based on multiple instance
learning (MIL) and Boosting techniques and the experiments on real world images and video sequences demonstrate the advantages of the approach and its superior performances over the state-of-the-art approaches.There are three contributions in this thesis. Firstly, we introduce a novel offline object detection approach which does not require manual annotation of training samples yet still has comparable discriminative power to
supervised learning approaches. In the proposed approach, object hypotheses are annotated from images by a classical approach; then each object hypothesis' probability of being a true object of interest (denoted as soft label), is estimated as a multiple instance
learning (MIL) problem; next, the object hypotheses and their soft labels are used to train an object detector based on a proposed Boosting algorithm. Secondly, to take advantage of both weakly
supervised learning and online
learning, an online weakly
supervised object detection approach is proposed. The soft labels of streaming data are estimated and then a proposed online Boosting algorithm is applied to construct and update a Boosting classifier with the streaming data and their soft labels. Moreover, identifying positive instances in positive bags under MIL settings can be beneficial to WSOD. It can work as an automatic annotator and save a huge amount of manual annotation cost. It also provides a flexible way to train different classifiers based on the annotated instances for WSOD. To solve the MIL problem of labelling all instances in positive bags instead of just the bags alone, we propose a novel soft label estimation algorithm based on expectation maximization (EM).
Advisors/Committee Members: Sowmya, Arcot, Computer Science & Engineering, Faculty of Engineering, UNSW, Wang, Yang, Data61, Chen, Fang, Computer Science & Engineering, Faculty of Engineering, UNSW.
Subjects/Keywords: Boosting; Weakly supervised learning; Multiple instance learning; Object detection; Online learning
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Wang, W. (2016). A Weakly Supervised Approach for Object Detection. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/56619 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:41053/SOURCE02?view=true
Chicago Manual of Style (16th Edition):
Wang, Weihong. “A Weakly Supervised Approach for Object Detection.” 2016. Doctoral Dissertation, University of New South Wales. Accessed November 20, 2019.
http://handle.unsw.edu.au/1959.4/56619 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:41053/SOURCE02?view=true.
MLA Handbook (7th Edition):
Wang, Weihong. “A Weakly Supervised Approach for Object Detection.” 2016. Web. 20 Nov 2019.
Vancouver:
Wang W. A Weakly Supervised Approach for Object Detection. [Internet] [Doctoral dissertation]. University of New South Wales; 2016. [cited 2019 Nov 20].
Available from: http://handle.unsw.edu.au/1959.4/56619 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:41053/SOURCE02?view=true.
Council of Science Editors:
Wang W. A Weakly Supervised Approach for Object Detection. [Doctoral Dissertation]. University of New South Wales; 2016. Available from: http://handle.unsw.edu.au/1959.4/56619 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:41053/SOURCE02?view=true
Georgia Tech
26.
Berlind, Christopher.
New insights on the power of active learning.
Degree: PhD, Computer Science, 2015, Georgia Tech
URL: http://hdl.handle.net/1853/53948
► Traditional supervised machine learning algorithms are expected to have access to a large corpus of labeled examples, but the massive amount of data available in…
(more)
▼ Traditional
supervised machine
learning algorithms are expected to have access to a large corpus of labeled examples, but the massive amount of data available in the modern world has made unlabeled data much easier to acquire than accompanying labels. Active
learning is an extension of the classical paradigm intended to lessen the expense of the labeling process by allowing the
learning algorithm to intelligently choose which examples should be labeled.
In this dissertation, we demonstrate that the power to make adaptive label queries has benefits beyond reducing labeling effort over passive
learning. We develop and explore several novel methods for active
learning that exemplify these new capabilities. Some of these methods use active
learning for a non-standard purpose, such as computational speedup, structure discovery, and domain adaptation. Others successfully apply active
learning in situations where prior results have given evidence of its ineffectiveness.
Specifically, we first give an active algorithm for
learning disjunctions that is able to overcome a computational intractability present in the semi-
supervised version of the same problem. This is the first known example of the computational advantages of active
learning. Next, we investigate using active
learning to determine structural properties (margins) of the data-generating distribution that can further improve
learning rates. This is in contrast to most active
learning algorithms which either assume or ignore structure rather than seeking to identify and exploit it. We then give an active nearest neighbors algorithm for domain adaptation, the task of
learning a predictor for some target domain using mostly examples from a different source domain. This is the first formal analysis of the generalization and query behavior of an active domain adaptation algorithm. Finally, we show a situation where active
learning can outperform passive
learning on very noisy data, circumventing prior results that active
learning cannot have a significant advantage over passive
learning in high-noise regimes.
Advisors/Committee Members: Balcan, Maria-Florina (advisor), Song, Le (advisor), Vempala, Santosh (committee member), Blum, Avrim (committee member), Isbell, Charles L. (committee member).
Subjects/Keywords: Machine learning; Learning theory; Active learning; Semi-supervised learning; Domain adaptation; Large margin learning
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Berlind, C. (2015). New insights on the power of active learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53948
Chicago Manual of Style (16th Edition):
Berlind, Christopher. “New insights on the power of active learning.” 2015. Doctoral Dissertation, Georgia Tech. Accessed November 20, 2019.
http://hdl.handle.net/1853/53948.
MLA Handbook (7th Edition):
Berlind, Christopher. “New insights on the power of active learning.” 2015. Web. 20 Nov 2019.
Vancouver:
Berlind C. New insights on the power of active learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Nov 20].
Available from: http://hdl.handle.net/1853/53948.
Council of Science Editors:
Berlind C. New insights on the power of active learning. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53948
Carnegie Mellon University
27.
Zhang, Yi.
Learning with Limited Supervision by Input and Output Coding.
Degree: 2012, Carnegie Mellon University
URL: http://repository.cmu.edu/dissertations/156
► In many real-world applications of supervised learning, only a limited number of labeled examples are available because the cost of obtaining high-quality examples is high.…
(more)
▼ In many real-world applications of supervised learning, only a limited number of labeled examples are available because the cost of obtaining high-quality examples is high. Even with a relatively large number of labeled examples, the learning problem may still suffer from limited supervision as the complexity of the prediction function increases. Therefore, learning with limited supervision presents a major challenge to machine learning. With the goal of supervision reduction, this thesis studies the representation, discovery and incorporation of extra input and output information in learning.
Information about the input space can be encoded by regularization. We first design a semi-supervised learning method for text classification that encodes the correlation of words inferred from seemingly irrelevant unlabeled text. We then propose a multi-task learning framework with a matrix-normal penalty, which compactly encodes the covariance structure of the joint input space of multiple tasks. To capture structure information that is more general than covariance and correlation, we study a class of regularization penalties on model compressibility. Then we design the projection penalty, which encodes the structure information from a dimension reduction while controlling the risk of information loss.
Information about the output space can be exploited by error correcting output codes. Using the composite likelihood view, we propose an improved pairwise coding for multi-label classification, which encodes pairwise label density (as opposed to label comparisons) and decodes using variational methods. We then investigate problemdependent codes, where the encoding is learned from data instead of being predefined. We first propose a multi-label output code using canonical correlation analysis, where predictability of the code is optimized. We then argue that both discriminability and predictability are critical for output coding, and propose a max-margin formulation that promotes both discriminative and predictable codes.
We empirically study our methods in a wide spectrum of applications, including document categorization, landmine detection, face recognition, brain signal classification, handwritten digit recognition, house price forecasting, music emotion prediction, medical decision, email analysis, gene function classification, outdoor scene recognition, and so forth. In all these applications, our proposed methods for encoding input and output information lead to significantly improved prediction performance.
Subjects/Keywords: regularization; error-correcting output codes; supervised learning; semi-supervised learning; multi-task learning; multi-label classification; dimensionality reduction; Computer Sciences
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Zhang, Y. (2012). Learning with Limited Supervision by Input and Output Coding. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/156
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Zhang, Yi. “Learning with Limited Supervision by Input and Output Coding.” 2012. Thesis, Carnegie Mellon University. Accessed November 20, 2019.
http://repository.cmu.edu/dissertations/156.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Zhang, Yi. “Learning with Limited Supervision by Input and Output Coding.” 2012. Web. 20 Nov 2019.
Vancouver:
Zhang Y. Learning with Limited Supervision by Input and Output Coding. [Internet] [Thesis]. Carnegie Mellon University; 2012. [cited 2019 Nov 20].
Available from: http://repository.cmu.edu/dissertations/156.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Zhang Y. Learning with Limited Supervision by Input and Output Coding. [Thesis]. Carnegie Mellon University; 2012. Available from: http://repository.cmu.edu/dissertations/156
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Georgia
28.
Mahamuda, Vasim.
Analyzing the performance of machine learning algorithms on metagenomic data.
Degree: MS, Computer Science, 2010, University of Georgia
URL: http://purl.galileo.usg.edu/uga_etd/mahamuda_vasim_201008_ms
► Metagenomics is a branch of bioinformatics that deals with the study and analysis of micro-organisms in natural environments. Some micro-organisms including many species of bacteria,…
(more)
▼ Metagenomics is a branch of bioinformatics that deals with the study and analysis of micro-organisms in natural environments. Some micro-organisms including many species of bacteria, archea and viruses should be studied in their natural habitat as these organisms cannot be cultivated in the laboratory by using standard techniques. Machine
learning techniques are being applied to this field to predict novel genes. In this thesis, we try to address the issue of classifying metagenomic sequences. First, we compare the performance of several machine
learning approaches including ensemble learners to identify which algorithms will be able to bin metagenomic data into taxa-specific bins with high accuracy. Then we do scalability studies to investigate how the performance of those algorithms degrades as the number of species in the metagenomic sample increases. We also study the performance degradation with the increase in the number of unknown sequences in the data. The results are very promising and show that machine
learning algorithms perform very well in this domain. Futhermore, the performance degrades gracefully with the increase in the number of species and the number of unknown sequences.
Advisors/Committee Members: Khaled Rasheed.
Subjects/Keywords: Binning; decision trees; machine learning; metagenomics; ensemble methods; supervised learning.
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Mahamuda, V. (2010). Analyzing the performance of machine learning algorithms on metagenomic data. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/mahamuda_vasim_201008_ms
Chicago Manual of Style (16th Edition):
Mahamuda, Vasim. “Analyzing the performance of machine learning algorithms on metagenomic data.” 2010. Masters Thesis, University of Georgia. Accessed November 20, 2019.
http://purl.galileo.usg.edu/uga_etd/mahamuda_vasim_201008_ms.
MLA Handbook (7th Edition):
Mahamuda, Vasim. “Analyzing the performance of machine learning algorithms on metagenomic data.” 2010. Web. 20 Nov 2019.
Vancouver:
Mahamuda V. Analyzing the performance of machine learning algorithms on metagenomic data. [Internet] [Masters thesis]. University of Georgia; 2010. [cited 2019 Nov 20].
Available from: http://purl.galileo.usg.edu/uga_etd/mahamuda_vasim_201008_ms.
Council of Science Editors:
Mahamuda V. Analyzing the performance of machine learning algorithms on metagenomic data. [Masters Thesis]. University of Georgia; 2010. Available from: http://purl.galileo.usg.edu/uga_etd/mahamuda_vasim_201008_ms
The Ohio State University
29.
Sinha, Kaushik.
New Directions in Gaussian Mixture Learning and
Semi-supervised Learning.
Degree: PhD, Computer Science and Engineering, 2010, The Ohio State University
URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1284062001
► High dimensional data analysis involves, among many other tasks, modeling the unknown underlying data generating process and predicting one of the few possible sources…
(more)
▼ High dimensional data analysis involves,
among many other tasks, modeling the unknown underlying data
generating process and predicting one of the few possible sources
that might have generated the data. If data have some underlying
pattern or structure, it is conceivable that such a hidden pattern
or structure might provide important cues toward predicting the
possible data generating source. On the other hand, for modeling
purposes, it is important to understand the computational aspects
of the problem of
learning model parameters, especially the
dependence on dimension. This thesis addresses the computational
aspects of such a modeling problem especially in high dimension and
also describes a novel predictive framework that exploits the
underlying structure/pattern present in data. The
first part of this thesis addresses the problem of Gaussian mixture
learning. Gaussian mixture model is a fundamental model in applied
statistics and is a very popular choice for many
scientific/engineering modeling tasks. However, computational
aspects of
learning the parameters of this model, especially in
high dimension, is not well understood. The question of polynomial
learnability of probability distributions, particularly Gaussian
mixture distributions, has recently received significant attention
in theoretical computer science and
learning theory communities.
However, despite major progress, the general question of polynomial
learnability of Gaussian mixture distributions still remained open.
The result presented in this thesis resolves the question of
polynomial learnability for Gaussian mixtures in high dimension
with an arbitrary but fixed number of components.
The second part of this thesis describes studies
on semi-
supervised learning for multi-modal data, especially when
data have natural clusters. Many real world datasets in
scientific/engineering applications have clusters, e.g., hand
written digit dataset or breast cancer dataset. To deal with such
multi-modal data, this thesis proposes a novel framework which
establishes a natural connection between “cluster assumption” in
semi-
supervised learning and sparse approximation. Specifically,
when the number of labeled data is limited, the proposed method can
learn a classifier which has a sparse representation in an
appropriate set of bases and whose performance is comparable to the
state of the art semi-
supervised learning algorithms on a number of
real world datasets.
Advisors/Committee Members: Belkin, Mikhail (Advisor).
Subjects/Keywords: Computer Science; Gaussian Mixture Learning; Semi-supervised Learning
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Sinha, K. (2010). New Directions in Gaussian Mixture Learning and
Semi-supervised Learning. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1284062001
Chicago Manual of Style (16th Edition):
Sinha, Kaushik. “New Directions in Gaussian Mixture Learning and
Semi-supervised Learning.” 2010. Doctoral Dissertation, The Ohio State University. Accessed November 20, 2019.
http://rave.ohiolink.edu/etdc/view?acc_num=osu1284062001.
MLA Handbook (7th Edition):
Sinha, Kaushik. “New Directions in Gaussian Mixture Learning and
Semi-supervised Learning.” 2010. Web. 20 Nov 2019.
Vancouver:
Sinha K. New Directions in Gaussian Mixture Learning and
Semi-supervised Learning. [Internet] [Doctoral dissertation]. The Ohio State University; 2010. [cited 2019 Nov 20].
Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1284062001.
Council of Science Editors:
Sinha K. New Directions in Gaussian Mixture Learning and
Semi-supervised Learning. [Doctoral Dissertation]. The Ohio State University; 2010. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1284062001
Oregon State University
30.
Ashenfelter, Adam J.
Sequential supervised learning and conditional random fields.
Degree: MS, Computer Science, 2003, Oregon State University
URL: http://hdl.handle.net/1957/10165
► Supervised learning is concerned with discovering the relationship between example sets of features and their corresponding classes. The traditional supervised learning formulation assumes that all…
(more)
▼ Supervised learning is concerned with discovering the relationship between example sets of features and their corresponding classes. The traditional
supervised learning formulation assumes that all examples are independent from one another. The order of the examples contains no information. Nonetheless, many problems have a sequential nature. Classifiers for these problems must use the sequence of the examples, as well as the regular features, when generating a prediction. Sequential
supervised learning (SSL) algorithms are able to capture both types of information. A variety of sequential
supervised learning methods have already exist. The conditional random field is a particularly robust SSL algorithm that overcomes some limitations of other SSL methods, such as hidden Markov models and recurrent sliding windows. However, the
original implementation of the conditional random field explicitly represents a weight for every feature combination. For sequential problems using a window of input features, this means a combinatorial explosion. A better way to represent the conditional random field,
using regression trees, is introduced. The regression tree conditional random field provides an efficient method for
learning feature weights and supports the selection and combination of features. The ability to select feature combinations benefits classification performance.
Advisors/Committee Members: Dietterich, Thomas G. (advisor).
Subjects/Keywords: Supervised learning (Machine learning)
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Ashenfelter, A. J. (2003). Sequential supervised learning and conditional random fields. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/10165
Chicago Manual of Style (16th Edition):
Ashenfelter, Adam J. “Sequential supervised learning and conditional random fields.” 2003. Masters Thesis, Oregon State University. Accessed November 20, 2019.
http://hdl.handle.net/1957/10165.
MLA Handbook (7th Edition):
Ashenfelter, Adam J. “Sequential supervised learning and conditional random fields.” 2003. Web. 20 Nov 2019.
Vancouver:
Ashenfelter AJ. Sequential supervised learning and conditional random fields. [Internet] [Masters thesis]. Oregon State University; 2003. [cited 2019 Nov 20].
Available from: http://hdl.handle.net/1957/10165.
Council of Science Editors:
Ashenfelter AJ. Sequential supervised learning and conditional random fields. [Masters Thesis]. Oregon State University; 2003. Available from: http://hdl.handle.net/1957/10165
◁ [1] [2] [3] [4] [5] … [22] ▶
. 
Open Access Theses and Dissertations
